The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations

TL;DR

The paper tackles the challenge of analyzing large-scale democratic consultations with AI while ensuring transparency and accountability. It introduces Corpus Clarification, a three-step preprocessing pipeline to convert noisy, multi-topic citizen contributions into self-contained argumentative units: AU extraction, AS detection, and AU clarification. It presents GDN-CC, a manually annotated French dataset of 1,231 contributions (2,285 AUs), and GDN-CC-large with 240k auto-annotated contributions, and shows finetuned Small Language Models can match or exceed large API LLMs in producing clarifications, enabling scalable, transparent analysis. The work demonstrates substantial downstream gains for clustering and topic modeling, offering a resource for political science and NLP research, and discusses limitations and ethical considerations of standardization in democratic contexts.

Abstract

LLMs are ubiquitous in modern NLP, and while their applicability extends to texts produced for democratic activities such as online deliberations or large-scale citizen consultations, ethical questions have been raised for their usage as analysis tools. We continue this line of research with two main goals: (a) to develop resources that can help standardize citizen contributions in public forums at the pragmatic level, and make them easier to use in topic modeling and political analysis; (b) to study how well this standardization can reliably be performed by small, open-weights LLMs, i.e. models that can be run locally and transparently with limited resources. Accordingly, we introduce Corpus Clarification as a preprocessing framework for large-scale consultation data that transforms noisy, multi-topic contributions into structured, self-contained argumentative units ready for downstream analysis. We present GDN-CC, a manually-curated dataset of 1,231 contributions to the French Grand Débat National, comprising 2,285 argumentative units annotated for argumentative structure and manually clarified. We then show that finetuned Small Language Models match or outperform LLMs on reproducing these annotations, and measure their usability for an opinion clustering task. We finally release GDN-CC-large, an automatically annotated corpus of 240k contributions, the largest annotated democratic consultation dataset to date.

Figures (19)

• Figure 1: The Corpus Clarification task applied to one contribution. The original contribution (left) is segmented in topics, then in argumentative units (middle), which are automatically reformulated to generate clearer versions of the expressed opinions (right). The contribution was translated to English and simplified for illustrative purposes.
• Figure 2: Annotation interface. On the left is the segmented contribution, and on the right the clarification for each argumentative unit.
• Figure 3: Welcome page. See Figure \ref{['fig:explanation_translation']} for translation of the task explanation.
• Figure 4: Example page.
• Figure 5: Account page.